Journal article 1199 views 431 downloads
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders
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Cinzia Giannetti
IEEE Transactions on Industrial Informatics, Volume: 16, Issue: 9, Pages: 6069 - 6078
Swansea University Authors:
Aniekan Essien , Cinzia Giannetti
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DOI (Published version): 10.1109/tii.2020.2967556
Abstract
Timeseries forecasting is applied to many areas of smart factories, including machine health monitoring (MHM), predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust production processes based on different system conditions,...
| Published in: | IEEE Transactions on Industrial Informatics |
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| ISSN: | 1551-3203 1941-0050 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2020
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa53318 |
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| Abstract: |
Timeseries forecasting is applied to many areas of smart factories, including machine health monitoring (MHM), predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust production processes based on different system conditions, optimise production throughput, and minimise energy consumption. However, making accurate data-driven machine speed forecasts is challenging. Given the complex nature of industrial manufacturing processes, predictive models that are robust to noise and can capture the temporal and spatial distributions of the input timeseries are prerequisites for accurate forecasting. Motivated by recent deep learning studies in smart manufacturing, this paper proposes an end-to-end model for multi-step-ahead machine speed prediction. The model, known as 2D-Convolutional LSTM Autoencoder (2DConvLSTMAE), comprises a deep convolutional LSTM (ConvLSTM) encoder-decoder architecture. Extensive empirical analyses using real-world data obtained from a metal packaging plant in the United Kingdom demonstrate the value of the proposed method when compared to state-of-the-art deep learning models. |
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| Keywords: |
deep learning, ConvLSTM, stacked autoencoders, timeseries prediction, Industry 4.0 |
| College: |
Faculty of Science and Engineering |
| Funders: |
UKRI, EP/S001387/1 |
| Issue: |
9 |
| Start Page: |
6069 |
| End Page: |
6078 |